0001: """ Non-negative matrix and tensor factorization

 0002: 

 0003: """

 0004: 

 0005: # Author: Paul Fogel

 0006: 

 0007: # License: MIT

 0008: # # Dec 28, '19

 0009: # Initialize progressbar

+0010: from tkinter import *

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+0011: from tkinter import ttk

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+0012: import pandas as pd

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+0015: from sklearn.utils.extmath import randomized_svd

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 0155:     Projected gradient

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 1077: 

+1078: def NMFSolve(M, Mmis, Mt0, Mw0, nc, tolerance, precision, LogIter, Status0, MaxIterations, NMFAlgo,

/* Python wrapper */
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static char __pyx_doc_4nmtf_7modules_9nmtf_base_26NMFSolve[] = "\n    Estimate left and right hand matrices\n    Input:\n         M: Input matrix\n         Mmis: Define missing values (0 = missing cell, 1 = real cell)\n         Mt0: Initial left hand matrix\n         Mw0: Initial right hand matrix\n         nc: NMF rank\n         tolerance: Convergence threshold\n         precision: Replace 0-value in multiplication rules\n         LogIter: Log results through iterations\n         Status0: Initial displayed status to be updated during iterations\n         MaxIterations: Max iterations\n         NMFAlgo: =1,3: Divergence; =2,4: Least squares;\n         NMFFixUserLHE: = 1 => fixed left hand matrix columns\n         NMFFixUserRHE: = 1 => fixed  right hand matrix columns\n         NMFMaxInterm: Max iterations for warmup multiplication rules\n         NMFMaxIterProj: Max iterations for projected gradient\n         NMFSparseLevel: Requested sparsity in terms of relative number of rows with 0 values in right hand matrix\n         NMFFindParts: Enforce convexity on left hand matrix\n         NMFFindCentroids: Enforce convexity on right hand matrix\n         NMFKernel: Type of kernel used; 1: linear; 2: quadratic; 3: radial\n         NMFReweighColumns: Reweigh columns in 2nd step of parts-based NMF\n         NMFPriors: Priors on right hand matrix\n         flagNonconvex: Non-convexity flag on left hand matrix\n    Output:\n         Mt: Left hand matrix\n         Mev: Scaling matrix\n         Mw: Right hand matrix\n         diff: objective cost\n         Mh: Convexity matrix\n         NMFPriors: Updated priors on right hand matrix\n         flagNonconvex: Updated non-convexity flag on left hand matrix\n    ";
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 1080:              NMFFindParts, NMFFindCentroids, NMFKernel, NMFReweighColumns, NMFPriors, flagNonconvex, AddMessage,

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static char __pyx_doc_4nmtf_7modules_9nmtf_base_28rNMFSolve[] = "\n    Estimate left and right hand matrices (robust version)\n    Input:\n         M: Input matrix\n         Mmis: Define missing values (0 = missing cell, 1 = real cell)\n         Mt0: Initial left hand matrix\n         Mw0: Initial right hand matrix\n         nc: NMF rank\n         tolerance: Convergence threshold\n         precision: Replace 0-values in multiplication rules\n         LogIter: Log results through iterations\n          MaxIterations: Max iterations\n         NMFAlgo: =1,3: Divergence; =2,4: Least squares;\n         NMFFixUserLHE: = 1 => fixed left hand matrix columns\n         NMFFixUserRHE: = 1 => fixed  right hand matrix columns\n         NMFMaxInterm: Max iterations for warmup multiplication rules\n         NMFSparseLevel: Requested sparsity in terms of relative number of rows with 0 values in right hand matrix\n         NMFRobustResampleColumns: Resample columns during bootstrap\n         NMFRobustNRuns: Number of bootstrap runs\n         NMFCalculateLeverage: Calculate leverages\n         NMFUseRobustLeverage: Calculate leverages based on robust max across factoring columns\n         NMFFindParts: Enforce convexity on left hand matrix\n         NMFFindCentroids: Enforce convexity on right hand matrix\n         NMFKernel: Type of kernel used; 1: linear; 2: quadraitc; 3: radial\n         NMFReweighColumns: Reweigh columns in 2nd step of parts-based NMF\n         NMFPriors: Priors on right hand matrix\n    Output:\n         Mt: Left hand matrix\n         Mev: Scaling matrix\n         Mw: Right hand matrix\n         MtPct: Percent robust clustered rows\n         MwPct: Percent robust clustered columns\n         diff: Objective cost\n         Mh: Convexity matrix\n         flagNonconvex: Updated non-convexity flag on left hand matrix\n    ";
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 1772:          nc: NTF rank

 1773:          tolerance: Convergence threshold

 1774:          precision: Replace 0-values in multiplication rules

 1775:          LogIter: Log results through iterations

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 1778:          Mw: Right hand matrix

 1779:          Mb: Block hand matrix

 1780:      """

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 2113:     """

 2114:     Estimate NTF matrices (HALS)

 2115:     Input:

 2116:          M: Input matrix

 2117:          Mmis: Define missing values (0 = missing cell, 1 = real cell)

 2118:          Mt0: Initial left hand matrix

 2119:          Mw0: Initial right hand matrix

 2120:          Mb0: Initial block hand matrix

 2121:          nc: NTF rank

 2122:          tolerance: Convergence threshold

 2123:          LogIter: Log results through iterations

 2124:          Status0: Initial displayed status to be updated during iterations

 2125:          MaxIterations: Max iterations

 2126:          NMFFixUserLHE: = 1 => fixed left hand matrix columns

 2127:          NMFFixUserRHE: = 1 => fixed  right hand matrix columns

 2128:          NMFFixUserBHE: = 1 => fixed  block hand matrix columns

 2129:          NTFUnimodal: Apply Unimodal constraint on factoring vectors

 2130:          NTFSmooth: Apply Smooth constraint on factoring vectors

 2131:          NTFLeftComponents: Apply Unimodal/Smooth constraint on left hand matrix

 2132:          NTFRightComponents: Apply Unimodal/Smooth constraint on right hand matrix

 2133:          NTFBlockComponents: Apply Unimodal/Smooth constraint on block hand matrix

 2134:          NBlocks: Number of NTF blocks

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 2136:          Mt: Left hand matrix

 2137:          Mw: Right hand matrix

 2138:          Mb: Block hand matrix

 2139:          Mres: Residual tensor

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 2724: 

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 3221:                                leverage='standard',

+3222:                                convex=None, kernel='linear',

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 3226:                                verbose=0):

 3227:     """Compute Non-negative Matrix Factorization (NMF)

 3228: 

 3229:     Find two non-negative matrices (W, H) such as x = W @ H.T + Error.

 3230:     This factorization can be used for example for

 3231:     dimensionality reduction, source separation or topic extraction.

 3232: 

 3233:     The objective function is minimized with an alternating minimization of W

 3234:     and H.

 3235: 

 3236:     Parameters

 3237:     ----------

 3238: 

 3239:     X : array-like, shape (n_samples, n_features)

 3240:         Constant matrix.

 3241: 

 3242:     W : array-like, shape (n_samples, n_components)

 3243:         prior W

 3244:         If n_update_W == 0 , it is used as a constant, to solve for H only.

 3245: 

 3246:     H : array-like, shape (n_features, n_components)

 3247:         prior H

 3248:         If n_update_H = 0 , it is used as a constant, to solve for W only.

 3249: 

 3250:     n_components : integer

 3251:         Number of components, if n_components is not set : n_components = min(n_samples, n_features)

 3252: 

 3253:     update_W : boolean, default: True

 3254:         Update or keep W fixed

 3255: 

 3256:     update_H : boolean, default: True

 3257:         Update or keep H fixed

 3258: 

 3259:     beta_loss : string, default 'frobenius'

 3260:         String must be in {'frobenius', 'kullback-leibler'}.

 3261:         Beta divergence to be minimized, measuring the distance between X

 3262:         and the dot product WH. Note that values different from 'frobenius'

 3263:         (or 2) and 'kullback-leibler' (or 1) lead to significantly slower

 3264:         fits. Note that for beta_loss == 'kullback-leibler', the input

 3265:         matrix X cannot contain zeros.

 3266: 

 3267:     n_bootstrap : integer, default: 0

 3268:         Number of bootstrap runs.

 3269: 

 3270:     tol : float, default: 1e-6

 3271:         Tolerance of the stopping condition.

 3272: 

 3273:     max_iter : integer, default: 200

 3274:         Maximum number of iterations.

 3275: 

 3276:     max_iter_mult : integer, default: 20

 3277:         Maximum number of iterations in multiplicative warm-up to projected gradient (beta_loss = 'frobenius' only).

 3278: 

 3279:     regularization :  None | 'components' | 'transformation'

 3280:         Select whether the regularization affects the components (H), the

 3281:         transformation (W) or none of them.

 3282: 

 3283:     sparsity : double, default: 0.

 3284:         Sparsity target with 0 <= sparsity <= 1 representing the % rows in W or H set to 0.

 3285: 

 3286:     leverage :  None | 'standard' | 'robust', default 'standard'

 3287:         Calculate leverage of W and H rows on each component.

 3288: 

 3289:     convex :  None | 'components' | 'transformation', default None

 3290:         Apply convex constraint on W or H.

 3291: 

 3292:     kernel :  'linear', 'quadratic', 'radial', default 'linear'

 3293:         Can be set if convex = 'transformation'.

 3294: 

 3295:     null_priors : boolean, default False

 3296:         Cells of H with prior cells = 0 will not be updated.

 3297:         Can be set only if prior H has been defined.

 3298: 

 3299:     skewness : boolean, default False

 3300:         When solving mixture problems, columns of X at the extremities of the convex hull will be given largest weights.

 3301:         The column weight is a function of the skewness and its sign.

 3302:         The expected sign of the skewness is based on the skewness of W components, as returned by the first pass

 3303:         of a 2-steps convex NMF. Thus, during the first pass, skewness must be set to False.

 3304:         Can be set only if convex = 'transformation' and prior W and H have been defined.

 3305: 

 3306:     random_state : int, RandomState instance or None, optional, default: None

 3307:         If int, random_state is the seed used by the random number generator;

 3308:         If RandomState instance, random_state is the random number generator;

 3309:         If None, the random number generator is the RandomState instance used

 3310:         by `np.random`.

 3311: 

 3312:     verbose : integer, default: 0

 3313:         The verbosity level (0/1).

 3314: 

 3315: 

 3316:     Returns

 3317:     -------

 3318: 

 3319:     Estimator (dictionary) with following entries

 3320: 

 3321:     W : array-like, shape (n_samples, n_components)

 3322:         Solution to the non-negative least squares problem.

 3323: 

 3324:     H : array-like, shape (n_features, n_components)

 3325:         Solution to the non-negative least squares problem.

 3326: 

 3327:     V : scalar, volume occupied by W and H

 3328: 

 3329:     WB : array-like, shape (n_samples, n_components)

 3330:         Percent consistently clustered rows for each component.

 3331:         only if n_bootstrap > 0.

 3332: 

 3333:     HB : array-like, shape (n_features, n_components)

 3334:         Percent consistently clustered columns for each component.

 3335:         only if n_bootstrap > 0.

 3336: 

 3337:     B : array-like, shape (n_observations, n_components) or (n_features, n_components)

 3338:         only if active convex variant, H = B.T @ X or W = X @ B

 3339: 

 3340: 

 3341:     Examples

 3342:     --------

 3343: 

 3344:     >>> import numpy as np

 3345: 

 3346:     >>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])

 3347: 

 3348:     >>> from sklearn.decomposition import non_negative_factorization

 3349: 

 3350:     >>> W, H, n_iter = non_negative_factorization(X, n_components=2, \

 3351: 

 3352:         random_state=0)

 3353: 

 3354: 

 3355:     References

 3356:     ----------

 3357: 

 3358:     Fogel

 3359: 

 3360:     Lin

 3361:     """

 3362: 

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 3533:     """Derives from factorization result ordered sample and feature indexes for future use in ordered heatmaps

 3534: 

 3535:     Parameters

 3536:     ----------

 3537: 

 3538:     estimator : tuplet as returned by non_negative_factorization

 3539: 

 3540:     leverage :  None | 'standard' | 'robust', default 'robust'

 3541:         Calculate leverage of W and H rows on each component.

 3542: 

 3543:     blocks : array-like, shape(n_blocks), default None

 3544:         Size of each block (if any) in ordered heatmap.

 3545: 

 3546:     cluster_by_stability : boolean, default False

 3547:          Use stability instead of leverage to assign samples/features to clusters

 3548: 

 3549:     custom_order :  boolean, default False

 3550:          if False samples/features with highest leverage or stability appear on top of each cluster

 3551:          if True within cluster ordering is modified to suggest a continuum  between adjacent clusters

 3552: 

 3553:     verbose : integer, default: 0

 3554:         The verbosity level (0/1).

 3555: 

 3556: 

 3557:     Returns

 3558:     -------

 3559: 

 3560:     Completed estimator with following entries:

 3561:     WL : array-like, shape (n_samples, n_components)

 3562:          Sample leverage on each component

 3563: 

 3564:     HL : array-like, shape (n_features, n_components)

 3565:          Feature leverage on each component

 3566: 

 3567:     FL : array-like, shape (n_blocks, n_components)

 3568:          Block leverage on each component (NTF only)

 3569: 

 3570:     WR : vector-like, shape (n_samples)

 3571:          Ranked sample indexes (by cluster and leverage or stability)

 3572:          Used to produce ordered heatmaps

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 3575:          Ranked feature indexes (by cluster and leverage or stability)

 3576:          Used to produce ordered heatmaps

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 3578:     WN : vector-like, shape (n_components)

 3579:          Sample cluster bounds in ordered heatmap

 3580: 

 3581:     HN : vector-like, shape (n_components)

 3582:          Feature cluster bounds in ordered heatmap

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 3584:     WC : vector-like, shape (n_samples)

 3585:          Sample assigned cluster

 3586: 

 3587:     HC : vector-like, shape (n_features)

 3588:          Feature assigned cluster

 3589: 

 3590:     FC : vector-like, shape (size(blocks))

 3591:          Block assigned cluster (NTF only)

 3592: 

 3593:     Examples

 3594:     --------

 3595: 

 3596:     >>> import numpy as np

 3597: 

 3598:     """

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 3668:     """Derives from factorization result ordered sample and feature indexes for future use in ordered heatmaps

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static char __pyx_doc_4nmtf_7modules_9nmtf_base_56non_negative_tensor_factorization[] = "Compute Non-negative Tensor Factorization (NTF)\n\n    Find three non-negative matrices (W, H, F) such as x = W @@ H @@ F + Error (@@ = tensor product).\n    This factorization can be used for example for\n    dimensionality reduction, source separation or topic extraction.\n\n    The objective function is minimized with an alternating minimization of W\n    and H.\n\n    Parameters\n    ----------\n\n    X : array-like, shape (n_samples, n_features x n_blocks)\n        Constant matrix.\n        X is a tensor with shape (n_samples, n_features, n_blocks), however unfolded along 2nd and 3rd dimensions.\n\n    n_blocks : integer\n\n    W : array-like, shape (n_samples, n_components)\n        prior W\n\n    H : array-like, shape (n_features, n_components)\n        prior H\n\n    F : array-like, shape (n_blocks, n_components)\n        prior H\n\n    n_components : integer\n        Number of components, if n_components is not set : n_components = min(n_samples, n_features)\n\n    update_W : boolean, default: True\n        Update or keep W fixed\n\n    update_H : boolean, default: True\n        Update or keep H fixed\n\n    update_F : boolean, default: True\n        Update or keep F fixed\n\n    fast_hals : boolean, default: True\n        Use fast implementation of HALS\n\n    n_iter_hals : integer, default: 2\n        Number of HALS iterations prior to fast HALS\n    \n    n_shift : integer, default: 0\n        max shifting in convolutional NTF\n\n    unimodal : Boolean, default: False\n\n    smooth : Boolean, default: False\n\n    apply_left : Boolean, default: False\n\n    apply_right : Boolean, default: False\n\n    apply_block : Boolean, default: False\n\n    n_bootstrap : integer, default: 0\n        Number of bootstrap runs.\n\n    tol : float, default: 1e-6\n        Tolerance of the stopping condition.\n\n    max_iter : integer, default: 200\n        Maximum number of iterations.\n\n    leverage :  None | 'standard' | 'robust', default 'standard'\n        Calculate"" leverage of W and H rows on each component.\n\n    random_state : int, RandomState instance or None, optional, default: None\n        If int, random_state is the seed used by the random number generator;\n        If RandomState instance, random_state is the random number generator;\n        If None, the random number generator is the RandomState instance used\n        by `np.random`.\n\n    verbose : integer, default: 0\n        The verbosity level (0/1).\n\n\n    Returns\n    -------\n\n        Estimator (dictionary) with following entries\n\n        W : array-like, shape (n_samples, n_components)\n            Solution to the non-negative least squares problem.\n\n        H : array-like, shape (n_features, n_components)\n            Solution to the non-negative least squares problem.\n\n        F : array-like, shape (n_blocks, n_components)\n            Solution to the non-negative least squares problem.\n        \n        E : array-like, shape (n_samples, n_features x n_blocks)\n            E is the residual tensor with shape (n_samples, n_features, n_blocks), however unfolded along 2nd and 3rd dimensions.\n        \n        V : scalar, volume occupied by W and H\n        \n        WB : array-like, shape (n_samples, n_components)\n            Percent consistently clustered rows for each component.\n            only if n_bootstrap > 0.\n\n        HB : array-like, shape (n_features, n_components)\n            Percent consistently clustered columns for each component.\n            only if n_bootstrap > 0.\n\n    Examples\n    --------\n\n    >>> import numpy as np\n\n    >>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])\n\n    >>> from sklearn.decomposition import non_negative_factorization\n\n    >>> W, H, n_iter = non_negative_factorization(X, n_components=2, \n        random_state=0)\n\n\n    References\n    ----------\n\n    Fogel\n\n    Lin\n    ";
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 3745:                                       verbose=0):

 3746:     """Compute Non-negative Tensor Factorization (NTF)

 3747: 

 3748:     Find three non-negative matrices (W, H, F) such as x = W @@ H @@ F + Error (@@ = tensor product).

 3749:     This factorization can be used for example for

 3750:     dimensionality reduction, source separation or topic extraction.

 3751: 

 3752:     The objective function is minimized with an alternating minimization of W

 3753:     and H.

 3754: 

 3755:     Parameters

 3756:     ----------

 3757: 

 3758:     X : array-like, shape (n_samples, n_features x n_blocks)

 3759:         Constant matrix.

 3760:         X is a tensor with shape (n_samples, n_features, n_blocks), however unfolded along 2nd and 3rd dimensions.

 3761: 

 3762:     n_blocks : integer

 3763: 

 3764:     W : array-like, shape (n_samples, n_components)

 3765:         prior W

 3766: 

 3767:     H : array-like, shape (n_features, n_components)

 3768:         prior H

 3769: 

 3770:     F : array-like, shape (n_blocks, n_components)

 3771:         prior H

 3772: 

 3773:     n_components : integer

 3774:         Number of components, if n_components is not set : n_components = min(n_samples, n_features)

 3775: 

 3776:     update_W : boolean, default: True

 3777:         Update or keep W fixed

 3778: 

 3779:     update_H : boolean, default: True

 3780:         Update or keep H fixed

 3781: 

 3782:     update_F : boolean, default: True

 3783:         Update or keep F fixed

 3784: 

 3785:     fast_hals : boolean, default: True

 3786:         Use fast implementation of HALS

 3787: 

 3788:     n_iter_hals : integer, default: 2

 3789:         Number of HALS iterations prior to fast HALS

 3790:     

 3791:     n_shift : integer, default: 0

 3792:         max shifting in convolutional NTF

 3793: 

 3794:     unimodal : Boolean, default: False

 3795: 

 3796:     smooth : Boolean, default: False

 3797: 

 3798:     apply_left : Boolean, default: False

 3799: 

 3800:     apply_right : Boolean, default: False

 3801: 

 3802:     apply_block : Boolean, default: False

 3803: 

 3804:     n_bootstrap : integer, default: 0

 3805:         Number of bootstrap runs.

 3806: 

 3807:     tol : float, default: 1e-6

 3808:         Tolerance of the stopping condition.

 3809: 

 3810:     max_iter : integer, default: 200

 3811:         Maximum number of iterations.

 3812: 

 3813:     leverage :  None | 'standard' | 'robust', default 'standard'

 3814:         Calculate leverage of W and H rows on each component.

 3815: 

 3816:     random_state : int, RandomState instance or None, optional, default: None

 3817:         If int, random_state is the seed used by the random number generator;

 3818:         If RandomState instance, random_state is the random number generator;

 3819:         If None, the random number generator is the RandomState instance used

 3820:         by `np.random`.

 3821: 

 3822:     verbose : integer, default: 0

 3823:         The verbosity level (0/1).

 3824: 

 3825: 

 3826:     Returns

 3827:     -------

 3828: 

 3829:         Estimator (dictionary) with following entries

 3830: 

 3831:         W : array-like, shape (n_samples, n_components)

 3832:             Solution to the non-negative least squares problem.

 3833: 

 3834:         H : array-like, shape (n_features, n_components)

 3835:             Solution to the non-negative least squares problem.

 3836: 

 3837:         F : array-like, shape (n_blocks, n_components)

 3838:             Solution to the non-negative least squares problem.

 3839:         

 3840:         E : array-like, shape (n_samples, n_features x n_blocks)

 3841:             E is the residual tensor with shape (n_samples, n_features, n_blocks), however unfolded along 2nd and 3rd dimensions.

 3842:         

 3843:         V : scalar, volume occupied by W and H

 3844:         

 3845:         WB : array-like, shape (n_samples, n_components)

 3846:             Percent consistently clustered rows for each component.

 3847:             only if n_bootstrap > 0.

 3848: 

 3849:         HB : array-like, shape (n_features, n_components)

 3850:             Percent consistently clustered columns for each component.

 3851:             only if n_bootstrap > 0.

 3852: 

 3853:     Examples

 3854:     --------

 3855: 

 3856:     >>> import numpy as np

 3857: 

 3858:     >>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])

 3859: 

 3860:     >>> from sklearn.decomposition import non_negative_factorization

 3861: 

 3862:     >>> W, H, n_iter = non_negative_factorization(X, n_components=2, \

 3863: 

 3864:         random_state=0)

 3865: 

 3866: 

 3867:     References

 3868:     ----------

 3869: 

 3870:     Fogel

 3871: 

 3872:     Lin

 3873:     """

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